71–80 of 729 posts
The part that astonishes me is that in the year of our common era two thousand twenty-six that it's not universally offensive and reviling to post an AI-generated response to another person. If I'm reading something on the internet, I'm either reading it to learn, or I'm reading it to be persuaded. If I wanted the LLM to teach me (thank you, no), I would ask an LLM. I'm reading your website/newsletter/email because I…
I'll probably be downvoted to the bottom of the ocean, but I feel this needs to be said: - Why does it matter who or what wrote a thing? And how would you know if the /content/ is worthwhile, unless you read it... - The ability to "detect AI" is imperfect at best. 90% AI written? 5%? How would you know, unless you read it.... - If you didn't read it, then why brag about it with an "AI;DR"? - If you are wrong, and you…
If some text is AI written as a response to a much shorter prompt, then the prompt and/or sources used to make the text should be published instead (or at the very least together with the text).
I think of a prompt more like a tweet. The medium does matter. If someone was only willing or able to put an abbreviated amount of thought and effort into something, it's not worth a disproportionate amount my time or attention.
AI—DR
Before I think he was using AI and other tools to translate his messages since english isn't his first language. His own words, then AI translated it. Sounded kinda clunky, but I could sense the human behind the words and I gave grace since I can only imagine how hard it is to properly communicate your ideas when English isn't your first language.
But now it's gotten to a point where I can tell he's not using it for just translation. I'm gonna need to chat with him.
Redacted example below
```Hey @PERSON_WHO_ASKED_QUESTION Both good, and the retrieval one isn't written down anywhere. In order.
One corpus or four. My lean is one. Same chunks and embeddings tables, source_type column to tell them apart.
Values I'd propose, flat rather than nested: paper, dataset_description, dataset_readme, dataset_contributors, dataset_records, dataset_files. Description and readme split because their units already differ, one row versus one row per paragraph. A discriminator that can't separate those isn't doing much. The existing 440 rows would need backfilling to paper.
Chunk id in the same spirit: dataset doi, source type, ord. So EXAMPLE_DOI.
Worth checking before any of this matters: does chunks.doi carry a foreign key to papers.doi in 0001_init.sql? If it does, a metadata chunk with a dataset doi can't go in that table at all, and separate storage stops being a choice. Ten second read, I haven't done it. Shout if you get there first.
Retrieval is the one that's bigger than it looks. Search once across everything and nothing guarantees a paper chunk and a metadata chunk both land in the top k. The facts we want relate the two, and the generator can only write those if it sees both sides in the same window. So if one type systematically wins the ranking, that class of fact doesn't get worse. It becomes impossible.
Which way it goes I don't know. Two mechanisms pull opposite ways. Metadata chunks are short, tens of tokens against roughly 450 for a paper chunk, so they may just lose. But we embed context header plus text, and on a short chunk the header is most of the vector. The header is the dataset name, which is also most of the query. That points the other way.
Cheaper to measure than argue. Load one dataset's metadata, run a normal dataset level query, record the rank of the first chunk of each source type. Runnable as soon as any one of our four subtasks lands.
After that it's one pool, per source with quotas, or one pool with a floor per source type. I'd rather not pick before there's a measurement. ```
My coworkers continue to dump hundreds of lines of AI documentation in every PR and every other line of code has between one and ten lines of AI generated comments, talking about the real unlock and how things are byte for byte identical on the load bearing path or how the acceptance ladder is misleading. Features are coming out and metrics are improving, but we’re basically in a post readability code base, with the…
> TL;DR (too long; didn’t read) was the solution for social media. > AI;DR (AI; didn’t read) is the solution for AI slop. Maybe I'm the odd one out here and didn't plumb the worse-depths of social media, but I feel I need to defend good old TLDR as a slightly different, and less-hostile animal. It can be a moral condemnation of the poster, where they're disrespecting everyone else's time by posting something big and…
I'd say that sounds about the same for ai;dr. I could see myself posting a prompt like that in front of the generated report.
I submit pretty much the opposite of this, but it didn't gain any traction: https://news.ycombinator.com/item?id=49332291
Why would you think it would?
My coworkers continue to dump hundreds of lines of AI documentation in every PR and every other line of code has between one and ten lines of AI generated comments, talking about the real unlock and how things are byte for byte identical on the load bearing path or how the acceptance ladder is misleading. Features are coming out and metrics are improving, but we’re basically in a post readability code base, with the…
Write REVIEW.md.
I have CC check itself pretty well.
I also put into agent/claude/review instructions to write using simple English skill and humanizer skill. Then not to write redundant comments.
It’s not perfect but definitely catches lots of slop.